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Brian J Spiesman1

  • 1Department of Entomology, Kansas State University, Manhattan, KS, USA.

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Artificial intelligence (AI) can enhance pollinator monitoring by integrating with participatory science (PS) programs. AI-powered image classification, coupled with expert verification, offers scalable and reliable solutions for tracking pollinator populations.

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Area of Science:

  • Ecology and Conservation Biology
  • Computational Biology and Bioinformatics
  • Data Science and Machine Learning

Background:

  • Global pollinator decline necessitates improved monitoring, but manual species identification is difficult to scale.
  • Participatory science (PS) generates vast pollinator data but suffers from biases and limited expert capacity.
  • Current monitoring methods struggle to meet the demands of understanding and addressing pollinator loss.

Purpose of the Study:

  • To explore the integration of artificial intelligence (AI), specifically computer vision, with PS for scalable pollinator monitoring.
  • To assess AI's potential in overcoming limitations of manual identification and expert capacity in PS programs.
  • To propose a framework for leveraging AI to enhance bee monitoring and conservation efforts.

Main Methods:

  • Utilizing computer vision-based AI models for detection and classification of pollinator species from images.
  • Integrating AI classifiers into expert verification pipelines with confidence-based filtering and advanced modeling techniques.
  • Addressing data gaps through targeted image collection and integration of diverse datasets.

Main Results:

  • AI image classifiers demonstrate high accuracy across numerous taxa and can be deployed on various platforms.
  • AI has the potential to significantly reduce expert workload while maintaining monitoring reliability.
  • Careful integration of AI into expert workflows can accelerate feedback loops for conservation.

Conclusions:

  • AI, particularly computer vision, can serve as a powerful tool to augment, not replace, taxonomic expertise in pollinator monitoring.
  • Integrating AI with PS offers a scalable and reliable approach to address challenges in pollinator conservation.
  • Further development requires addressing data limitations for rare species and standardizing AI integration protocols.